Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts
Bibliographic record
Abstract
Abstract Deep-sea sponges create hotspots of biodiversity and biological activity in the otherwise barren deep-sea. However, it remains elusive how sponge hosts and their microbial symbionts acquire and process food in these food-limited environments. Therefore, we traced the processing (i.e. assimilation and respiration) of 13C- and 15N-enriched dissolved organic matter (DOM) and bacteria by three dominant North Atlantic deep-sea sponges: the high microbial abundance (HMA) demosponge Geodia barretti, the low microbial abundance (LMA) demosponge Hymedesmia paupertas, and the LMA hexactinellid Vazella pourtalesii. We also assessed the assimilation of both food sources into sponge- and bacteria-specific phospholipid-derived fatty acid (PLFA) biomarkers. All sponges were capable of assimilating DOM as well as bacteria. However, processing of the two food sources differed considerably between the tested species: the DOM assimilation-to-respiration efficiency was highest for the HMA sponge, yet uptake rates were 4–5 times lower compared to LMA sponges. In contrast, bacteria were assimilated most efficiently and at the highest rate by the hexactinellid compared to the demosponges. Our results indicate that phylogeny and functional traits (e.g., abundance of microbial symbionts, morphology) influence food preferences and diet composition of sponges, which further helps to understand their role as key ecosystem engineers of deep-sea habitats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".